In order to maximize the number of signals that can share a transmission medium, the frequency spectrum is re-used in a variety of ways. The traditional approach is to physically isolate communications signals of the same frequency in order to reduce their mutual interference to acceptable levels. Less traditional approaches use spread spectrum techniques to average the effects of interference over a bandwidth significantly greater than the information bandwidth. In both of these cases, interference will exist to some extent and, in some cases, can significantly reduce the system capacity, i.e., the information/unit time/unit bandwidth.
To increase the capacity, joint detection schemes have been proposed that take into account the effect of the interference between the different signals and perform interference cancellation. Examples of such schemes are found in
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and include techniques such as applying linear transformations to the received samples to decorrelate the interference, and techniques such as estimating the strongest user first, subtracting it from the received signal and repeating it for the next strongest signal, etc. These techniques work well if the interference does not overwhelm the desired signal at any stage in the processing. Because of the latter constraint, these techniques have generally only been considered for spread spectrum signals. The aforementioned joint detection schemes do not take into account any forward error correction coding of the signals.
To achieve the theoretically optimum capacity when multiple signals share the same transmission medium requires the use of forward error correction coding as is described by T. M. Cover and J. A. Thomas, in Elements of Information Theory, New York: Wiley, 1991. Pedagogical techniques for achieving the theoretical capacity suggest applying a different code to each user at the transmitter and, at the receiver, estimating the digital signal with the largest signal to noise ratio (or the strongest code), subtracting its effect and then repeating for the next digital signal; very similar to the techniques which have been proposed for uncoded systems. These techniques require powerful codes that do not lead to a practical implementation. Such a technique, that is "almost practical", has been presented in the literature, for example by A. J. Viterbi, in a paper entitled "Very low rate convolutional codes for maximum theoretical performance of spread-spectrum multiple-access channels", IEEE J. Sel. Areas Comm., vol. 8, no.4, pp.641-649, May 1990, but it has the drawback that it treats the digital signals asymmetrically and requires some co-ordination between transmitters. An alternative approach to joint detection of multiple coded digital signals that is known to be optimum in a maximum likelihood sense for certain types of forward error correction codes, is described by T. R. Giallorenzi and S. G. Wilson, in a paper entitled "Multiuser ML sequence estimator for convolutional coded asynchronous DS-CDMA systems," IEEE Trans. Comm., vol. 44, No. 8, pp.997-1008, August 1996. This latter technique is a Viterbi-like algorithm that has a complexity, which is exponential in both the code memory and the number of digital signals, making it impractical for implementation. There are other approaches to the joint detection of multiple coded signals that are obvious to those practicing the art. These include approaches such as cascading a joint detector for multiple uncoded signals with standard decoding algorithm. These approaches however, are fundamentally limited by the performance of the joint detector for multiple uncoded signals.
Examples of schemes related to and for obtaining reliability estimates from the preliminary estimates are found in H. L. van Trees, Detection, Estimation and Modulation Theory:Part I, New York: Wiley, 1968.
Examples of schemes related to soft-output decoding are found in